End of training

README.md

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+---
+license: mit
+base_model: microsoft/deberta-v3-small
+tags:
+- generated_from_trainer
+metrics:
+- accuracy
+- f1
+- precision
+- recall
+model-index:
+- name: DeBERTaV3_model
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# DeBERTaV3_model
+
+This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.1419
+- Accuracy: 0.9615
+- F1: 0.8400
+- Precision: 0.875
+- Recall: 0.8077
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 5e-05
+- train_batch_size: 5
+- eval_batch_size: 5
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 10
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
+|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
+| No log        | 1.0   | 26   | 0.3810          | 0.875    | 0.0    | 0.0       | 0.0    |
+| No log        | 2.0   | 52   | 0.3740          | 0.875    | 0.0    | 0.0       | 0.0    |
+| No log        | 3.0   | 78   | 0.3303          | 0.875    | 0.0    | 0.0       | 0.0    |
+| No log        | 4.0   | 104  | 0.2997          | 0.875    | 0.0    | 0.0       | 0.0    |
+| No log        | 5.0   | 130  | 0.2484          | 0.8894   | 0.2581 | 0.8       | 0.1538 |
+| No log        | 6.0   | 156  | 0.1951          | 0.9375   | 0.6977 | 0.8824    | 0.5769 |
+| No log        | 7.0   | 182  | 0.1752          | 0.9423   | 0.7273 | 0.8889    | 0.6154 |
+| No log        | 8.0   | 208  | 0.1582          | 0.9519   | 0.7917 | 0.8636    | 0.7308 |
+| No log        | 9.0   | 234  | 0.1449          | 0.9615   | 0.8400 | 0.875     | 0.8077 |
+| No log        | 10.0  | 260  | 0.1419          | 0.9615   | 0.8400 | 0.875     | 0.8077 |
+
+
+### Framework versions
+
+- Transformers 4.41.2
+- Pytorch 2.3.0+cu121
+- Datasets 2.19.2
+- Tokenizers 0.19.1

added_tokens.json

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config.json

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+{
+  "_name_or_path": "microsoft/deberta-v3-small",
+  "architectures": [
+    "DebertaV2ForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "N/D",
+    "1": "staff",
+    "2": "clothes",
+    "3": "shop",
+    "4": "cash desk",
+    "5": "fitting rooms",
+    "6": "queue",
+    "7": "others"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "N/D": 0,
+    "cash desk": 4,
+    "clothes": 2,
+    "fitting rooms": 5,
+    "others": 7,
+    "queue": 6,
+    "shop": 3,
+    "staff": 1
+  },
+  "layer_norm_eps": 1e-07,
+  "max_position_embeddings": 512,
+  "max_relative_positions": -1,
+  "model_type": "deberta-v2",
+  "norm_rel_ebd": "layer_norm",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 6,
+  "pad_token_id": 0,
+  "pooler_dropout": 0,
+  "pooler_hidden_act": "gelu",
+  "pooler_hidden_size": 768,
+  "pos_att_type": [
+    "p2c",
+    "c2p"
+  ],
+  "position_biased_input": false,
+  "position_buckets": 256,
+  "problem_type": "multi_label_classification",
+  "relative_attention": true,
+  "share_att_key": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.41.2",
+  "type_vocab_size": 0,
+  "vocab_size": 128100
+}

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special_tokens_map.json

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tokenizer/special_tokens_map.json

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tokenizer/tokenizer_config.json

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